From: John Conover <john@email.johncon.com>
Subject: CIFE conferences
Date: Tue, 25 Feb 1997 03:20:20 -0800
The IEEE is sponsoring a lot of financial engineering conferences this
year. Attached is for the spring ...
John
BTW, most of the stuff is based on remedial stochastic calculus, and
optimization theory. The reason for IEEE's involvement is that most of
the stuff came from communications and information theory, ie., Bell
Labs from the time of Hartley Information through Shannon Information,
circa the 1920's through the late 1940's, (and Kelly's seminal 1957
interpretation of information and uncertainty and how it applied to
the financial markets and gambling, which created the field of
quantitative financial analysis, ie., programmed trading, or PT[1].)
BTW, (I can't resist,) Hartley and Shannon information have a peculiar
relation to electronics. The reason that the 2's number system works,
(you are reading this on a 2's compliment Boolean algebra machine,)
can be explained by Hartley information. (Hartley formalized the fact
that information is simply log-base-two of data states.) Shannon's
Master's thesis established the isomorphism between switches and the
Boolean algebras, of which Hartley information operating on data
states is one such. (Probably the most important Master's thesis ever
written-it is the theory of digital electronics. You see, your
computer can't really add, or subtract, or count-its all done through
Boolean algebra processes, which is a set of formulas that gives a
"prescription," telling the computer how to add, or subtract, etc.[2])
Right there is the theory of modern computing. And, also, why data and
information are different, and why computation and calculation are
different. The first machine proposed to do computation, (algorithmic
calculation, ie., a process of calculations, can be traced back to the
dark ages,) was Turing, in the late 1930's. It was a mathematical
machine, and although used in algorithm theory, (because it is the
most general information machine known,) but is not very practical for
other than theoretical purposes. Von Neumann proposed the first
practical implementation of an information machine, (the ENIAC was not
a computer, as first designed-it was a programmable calculator. Later
modifications made it an information machine, and the EDVAC was the
first true specifically designed machine to handle information, as
opposed to data.)
[1] The concept is simple. If you knew what the stock market was going
to do tomorrow, then you could make a mint. You have some concept of
what it is going to do, but there is some uncertainty about the
future, ie., you do not have exact information about the future. So,
how do you handle things in the face of information uncertainty? How
about like the error correction modem that you probably received this
message on? Same drama, only the cast of characters are different. (No
pun intended.) Your disk drive probably had a couple of errors while
you were reading this, too. (They are common, but the information
machine corrects them.) It too has to retrieve exact data with
information uncertainty. (Typically, a Reed-Solomon variant
information algorithm is used for this. Astonishingly, a hard disk
drive, or a modem, does not have to have all of the data to
reconstruct the data in its entirety. The Internet also uses the same
information technology-when running hot and heavy, only about a third
of the information on the Internet is not corrupt, packet collisions
being the main source of the information uncertainty. The study of
such things is technically termed Informatics, which is the study of
Claude Shannon's information theory.)
[2] For example, suppose you want to add two numbers. If you logically
OR (ie., logical operation, X or Y,) the two numbers together, (except
when both bits are true, in which case you set the bit to zero, and
generate a carry,) you have added the two numbers. (The technical term
for such a gizmo is a half adder, or an EXCLUSIVE OR operation.) If
you want to subtract two numbers, you invert all the bits, (called
COMPLEMENTING,) in one number, increment it, and then add the two
resulting numbers as above. The result is the subtraction of the
numbers. And you have the basic operations of an information
machine. Note that it was all done with algebraic operations-with no
arithmetic, per se. We can thank Claude Shannon for having the
intuition and insight that such things could be done
electronically. BTW, a good example of an EXCLUSIVE OR is the light
switches on stairs. If booth switches are off the light is off. If
both are on, the light is off. Otherwise, it is on, (ie, switch
positions different.) It might be just the opposite at your house,
depending on how your electrician did things, but you get the idea.
If you make a Boolean function that either increments, or decrements,
(ie., adds one or subtracts one,) a number, in a random fashion, and
plot the history of the number, it is a Brownian walk, fixed increment
fractal, that produces information uncertainty-what economists call an
ARCH, or GARCH model of financial uncertainty in the markets. For
theoretical arguments as to why markets operate that way, see the
stuff by Brian Arthur, "Complexity in Economic and Financial Markets,"
Complexity, 1, pp. 20-25, 1995. Also available from
http://www.santafe.edu/arthur. And that is why the electrical
engineers and quantitative economists, called "quants," are getting in
bed together.
------- start of digest (6 messages) (RFC 934 encapsulation) -------
From: payman@u.washington.edu (Payman Arabshahi)
To: John Conover <john@email.johncon.com>
Subject: CIFEr'97 Tutorial on Models for Stochastic Volatility - NY, March 23, 1997
Date: 25 Feb 1997 01:57:36 GMT
Message-ID: <5etgug$g9e@nntp1.u.washington.edu>
IEEE/IAFE Computational Intelligence in Financial Engineering Conference
CIFEr'97
March 23-25, 1997
Crowne Plaza Manhattan, New York City
http://www.ieee.org/nnc/cifer97
Registration information:
Barbara Klemm
CIFEr'97 Secretariat
Meeting Management
2603 Main Street, Suite # 690
Irvine, California 92714
Tel: (714) 752-8205 or
(800) 321-6338
Fax: (714) 752-7444
Email: Meetingmgt@aol.com
Tutorial 6 of 6
- ----------------------------------------------------------------------------
Models for Stochastic Volatility: Some Recent Developments
Nuno Cato
Professor, New Jersey Institute of Technology, Newark
Pedro J. F. de Lima
Professor, The Johns Hopkins University, Baltimore
In this tutorial, we will firstly discuss the importance of modeling stock
market's volatility. Secondly, we will review the basic properties of
GARCH- type and SV-type models and some of their most successful
extensions, namely the SWitching ARCH (SWARCH) models. The performance of
these models will be illustrated with some real data examples. Thirdly,
we will discuss some problems with the estimation of these models and with
their use for risk forecasting. Fourthly, we will describe some recent
research and some novel extensions to these models, such as the
Long-Memory Stochastic Volatility (LMSV) and the SWitching Stochastic
Volatility (SWSV) models. By using examples from recent stock market
behavior we illustrate the capabilities and shortcomings of these new
modeling and forecasting tools.
- ----------------------------------------------------------------------------
------------------------------
From: payman@u.washington.edu (Payman Arabshahi)
To: John Conover <john@email.johncon.com>
Subject: CIFEr'97 Tutorial on Time Series Tools for Finance - NY, March 23, 1997
Date: 25 Feb 1997 01:56:55 GMT
Message-ID: <5etgt7$g7a@nntp1.u.washington.edu>
IEEE/IAFE Computational Intelligence in Financial Engineering Conference
CIFEr'97
March 23-25, 1997
Crowne Plaza Manhattan, New York City
http://www.ieee.org/nnc/cifer97
Registration information:
Barbara Klemm
CIFEr'97 Secretariat
Meeting Management
2603 Main Street, Suite # 690
Irvine, California 92714
Tel: (714) 752-8205 or
(800) 321-6338
Fax: (714) 752-7444
Email: Meetingmgt@aol.com
Tutorial 4 of 6
- ----------------------------------------------------------------------------
Time Series Tools for Finance
Andreas Wiegend, Ph.D.
Professor, Stern School of Business, New York University
This tutorial presents a unifying view of the recent advances of
neuro-fuzzy, and other machine learning techniques for time series and
finance. It is given jointly by Prof. Andreas Wiegend (Stern School of
Business, NYU), and Dr. Georg Zimmerman (Siemens AG, Munich), and presents
both conceptual aspects of time series modeling, specific tricks for
financial engineering problems, and software engineering aspects for
building a trading system.
- ----------------------------------------------------------------------------
------------------------------
From: payman@u.washington.edu (Payman Arabshahi)
To: John Conover <john@email.johncon.com>
Subject: CIFEr'97 Tutorial on GARCH Time Series Modeling - NY, March 23, 1997
Date: 25 Feb 1997 01:56:33 GMT
Message-ID: <5etgsh$g78@nntp1.u.washington.edu>
IEEE/IAFE Computational Intelligence in Financial Engineering Conference
CIFEr'97
March 23-25, 1997
Crowne Plaza Manhattan, New York City
http://www.ieee.org/nnc/cifer97
Registration information:
Barbara Klemm
CIFEr'97 Secretariat
Meeting Management
2603 Main Street, Suite # 690
Irvine, California 92714
Tel: (714) 752-8205 or
(800) 321-6338
Fax: (714) 752-7444
Email: Meetingmgt@aol.com
Tutorial 3 of 6
- ----------------------------------------------------------------------------
GARCH Modeling of Financial Time Series
R. Douglas Martin, Ph.D.
Professor of Statistics, University of Washington
Chief Scientist, Data Analysis Products Division of MathSoft, Inc.
This tutorial provides an introduction to univariate and multivariate
generalized autoregressive heteroscedastic (GARCH) modeling of financial
returns time series data, with a focus on modeling conditional
volatilities and correlations. Basic aspects of the various models are
discussed, including: conditions for stationarity, optimization techniques
for maximum likelihood estimation of the models, use of the estimated
conditional standard deviations for value-at-risk calculations and options
pricing, use of conditional correlations in obtaining conditional
volatilities for portfolios. Examples are provided using the S+GARCH
object-oriented toolkit for GARCH modeling.
- ----------------------------------------------------------------------------
------------------------------
From: payman@u.washington.edu (Payman Arabshahi)
To: John Conover <john@email.johncon.com>
Subject: CIFEr'97 Tutorial on Evolutionary Computation - NY, March 23, 1997
Date: 25 Feb 1997 01:57:13 GMT
Message-ID: <5etgtp$g9c@nntp1.u.washington.edu>
IEEE/IAFE Computational Intelligence in Financial Engineering Conference
CIFEr'97
March 23-25, 1997
Crowne Plaza Manhattan, New York City
http://www.ieee.org/nnc/cifer97
Registration information:
Barbara Klemm
CIFEr'97 Secretariat
Meeting Management
2603 Main Street, Suite # 690
Irvine, California 92714
Tel: (714) 752-8205 or
(800) 321-6338
Fax: (714) 752-7444
Email: Meetingmgt@aol.com
Tutorial 5 of 6
- ----------------------------------------------------------------------------
An Introduction to Evolutionary Computation
David B. Fogel, PhD
Chief Scientist, Natural Selection, Inc., La Jolla
Evolutionary computation encompasses a broad field of optimization
algorithms that can be applied to diverse, difficult real-world problems.
It is particularly useful in addressing stochastic, nonlinear, and
time-varying optimization problems, including those arising in financial
engineering. This tutorial will provide background on the inspiration,
history, and the practical application of evolutionary computation to
problems typical of those encountered in financial engineering.
- ----------------------------------------------------------------------------
------------------------------
From: payman@u.washington.edu (Payman Arabshahi)
To: John Conover <john@email.johncon.com>
Subject: CIFEr'97 Tutorial on OTC Derivatives - NY, March 23, 1997
Date: 25 Feb 1997 01:56:02 GMT
Message-ID: <5etgri$g72@nntp1.u.washington.edu>
IEEE/IAFE Computational Intelligence in Financial Engineering Conference
CIFEr'97
March 23-25, 1997
Crowne Plaza Manhattan, New York City
http://www.ieee.org/nnc/cifer97
Registration information:
Barbara Klemm
CIFEr'97 Secretariat
Meeting Management
2603 Main Street, Suite # 690
Irvine, California 92714
Tel: (714) 752-8205 or
(800) 321-6338
Fax: (714) 752-7444
Email: Meetingmgt@aol.com
Tutorial 2 of 6
- ----------------------------------------------------------------------------
An Introduction to OTC Derivatives and Their Applications
John F. Marshall, Ph.D.
Executive Director
International Association of Financial Engineers
This tutorial is for persons with little prior exposure to derivative
instruments. It will focus on the basic products, how they trade, and how
they are used. It will be largely non-quantitative. The tutorial will
examine how derivatives are used by financial engineers for risk
management purposes, investment purposes, cash flow management, and
creating structured securities. The use of derivatives to circumvent
market imperfections, such as asymmetric taxes and transaction costs, will
also be demonstrated. The primary emphasis of the tutorial will be swaps
(including interest rate swaps, currency swaps, commodity swaps, equity
swaps, and macroeconomic swaps). Applications of OTC options, including
caps and floors and digital options will also be examined, but to a lesser
extent.
- ----------------------------------------------------------------------------
------------------------------
From: payman@u.washington.edu (Payman Arabshahi)
To: John Conover <john@email.johncon.com>
Subject: CIFEr'97 Tutorial on Risk Management - NY, March 23, 1997
Date: 25 Feb 1997 01:55:15 GMT
Message-ID: <5etgq3$g6e@nntp1.u.washington.edu>
IEEE/IAFE Computational Intelligence in Financial Engineering Conference
CIFEr'97
March 23-25, 1997
Crowne Plaza Manhattan, New York City
http://www.ieee.org/nnc/cifer97
Registration information:
Barbara Klemm
CIFEr'97 Secretariat
Meeting Management
2603 Main Street, Suite # 690
Irvine, California 92714
Tel: (714) 752-8205 or
(800) 321-6338
Fax: (714) 752-7444
Email: Meetingmgt@aol.com
Tutorial 1 of 6
- ----------------------------------------------------------------------------
Risk Management
Jan W. Dash, Ph.D.
Director
Quantitative Analysis
Global Risk Management
Smith Barney
This tutorial will cover 1) characterization of risks in finance: market
risk (interest rates, FX rates, equity indices, spreads), trading risk,
systems risk (software, hardware, vendors), model risk, and 2)
quantitative measurement of risk: the Greeks (Delta, Gamma, Vega), the
partial Greeks (Ladders), the new Greeks (Exotics), dollars at risk
(n-Sigma analysis), correlations, static scenario analysis, dynamic
scenario analysis, Monte Carlo risk analysis, beginnings of risk
standards, DPG, Risk Metrics, and 3) case study of risk: the Viacom CVR
Options and 4) pricing and hedging for interest rate derivatives.
- ----------------------------------------------------------------------------
------- end -------
--
John Conover, john@email.johncon.com, http://www.johncon.com/